To illustrate the capabilities of our trained regularizer, we first consider a limited-angle reconstruction problem.
Specifically, we reconstruct an image from \( n_\theta = {270} \)  projections uniformly spaced over the quarter-circle \( \theta \in [0, \frac{\pi }{2}] \) .
We show qualitative results in Fig. REF  (top), where the fbp reconstruction exhibits smearing artifacts that are characteristic of limited-angle ct.
sart [1]} and additional tv regularization help remedy this problem somewhat, however the reconstruction is not satisfactory.
We observe unnatural disconnected contours in the reconstruction, especially around the thorax.
On the contrary, our model is capable of reconstructing a natural looking image with realistic anatomy and high level of detail.
We show \( \mathbb {E}_{(f, x) \sim p_{\hat{x}}}[\operatorname{PSNR}(x^{*}(f), x)] \)  in Tab. REF .
The results are in accordance with the qualitative analysis, with our model improving the tv reconstruction by over 4.5.
